Modeling the effect of non‐linear process parameters on the prediction of hydrogen production by steam reforming of bio‐oil and glycerol using artificial neural network

Modeling the effect of non‐linear process parameters on the prediction of hydrogen production by steam reforming of bio‐oil and glycerol using artificial neural network

Summary Biomass‐derived substrates such as bio‐oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio‐oil obtained as by‐products of biodiesel production and pyro...

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Bibliographic Details
Published in:International journal of energy research Vol. 44; no. 13; pp. 10523 - 10537
Main Authors: Mageed, Alyaa K., Shnain, Zainab Yousif, Mahdi, Ghaidaa Saeed
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Inc 25-10-2020
Hindawi Limited
Subjects:
Acceptability
Algorithms
Artificial neural networks
Bayesian analysis
Biodiesel fuels
Biofuels
Biomass
bio‐oil
Configurations
Glycerol
Hydrogen
Hydrogen production
Mathematical models
Model testing
Modelling
Multilayers
Neural networks
Neurons
Nodes
Oil
Parameters
Probability theory
Process parameters
Pyrolysis
Reforming
Regularization
Steam
steam reforming
Substrates
Training
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Summary:Summary Biomass‐derived substrates such as bio‐oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio‐oil obtained as by‐products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non‐linear process parameters on the prediction of hydrogen production by biomass (bio‐oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio‐oil and glycerol steam reforming. Two algorithms namely Levenberg‐Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg‐Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio‐oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg‐Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio‐oil and glycerol steam agreed with the actual values with the coefficient of determination (R2) > 0.9. A low mean square error of 3.024 × 10−24 and 6.22 × 10−15 for the optimized for Levenberg‐Marquardt and Bayesian regularization‐trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol‐to‐water molar ratio were the most relevant factors that influenced the production of hydrogen by bio‐oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non‐linear process parameters on hydrogen production by bio‐oil and glycerol steam reforming.
ISSN:0363-907X
1099-114X
DOI:10.1002/er.5684